Evidence map›Paper›PMID 36273447›Full record

ArticleNephron2023

Modeling Low Muscle Mass Screening in Hemodialysis Patients.

Daiki Senzaki, Nobuo Yoshioka, Osamu Nagakawa, Emi Inayama, Takafumi Nakagawa, Hidehito Takayama, Toko Endo, Fumitaka Nakajima, Masayoshi Fukui, Yasuaki Kijima and 11 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nephron, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors at 11 institutions in 1 country.

Daiki SenzakiFujidera Keijin-kai Clinic, Fujidera, Japan.
Nobuo YoshiokaNishinokyou Hospital, Nara, Japan.
Osamu NagakawaMihama Sakura Clinic, Sakura, Japan.
Emi InayamaMihama Narita Clinic, Mihama, Japan.
Takafumi NakagawaHanwakinen Hospital, Osaka, Japan.
Hidehito TakayamaTakayama Clinic, Izumisano, Japan.
Toko EndoHN Medic, Sappor, Japan.
Fumitaka NakajimaKadoma Keijin-kai Clinic, Kadoma, Japan.
Masayoshi FukuiFukui Clinic, Kadoma, Japan.
Yasuaki KijimaKosumosu-kai Family Clinic, Osaka, Japan.
Yasuo OyamaNeyagawa Keijin-kai Clinic, Neyagawa, Japan.
Risshi KudoHamanasu Clinic, Ishikari, Japan.
Tadashi ToyamaKoryo Clinic, Takaoka, Japan.
Yosuke YamadaUeda Jinzou Clinic, Ueda, Japan.
Kiyoshi TsurusakiAsakayama Hospital, Sakai, Japan.
Naoki AoyamaMoriguchi Keijin-kai Clinic, Moriguchi, Japan.
Takayasu MatsumuraIkeda Hospital, Higashiosaka, Japan.
Hideki YamaharaFujidera Keijin-kai Clinic, Fujidera, Japan.
Kenro MiyasatoFujidera Keijin-kai Clinic, Fujidera, Japan.
Tetsuya KitamuraFujidera Keijin-kai Clinic, Fujidera, Japan.
Tatsuyoshi IkenoueFujidera Keijin-kai Clinic, Fujidera, Japan.
Fujifilm (Japan) · JPFukui CardioVascular Center · JPHamari Chemicals (Japan) · JPHanwa Memorial Hospital · JPIkeda Municipal Hospital · JPKanazawa University · JPMinamiaoyama Eye Clinic · JPNeyagawa High School · JPShiga University · JPShinshu University · JPTomitahama Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionComputed tomography (CT) can accurately measure muscle mass, which is necessary for diagnosing sarcopenia, even in dialysis patients. However, CT-based screening for such patients is challenging, especially considering the availability of equipment within dialysis facilities. We therefore aimed to develop a bedside prediction model for low muscle mass, defined by the psoas muscle mass index (PMI) from CT measurement.

methodsHemodialysis patients (n = 619) who had undergone abdominal CT screening were divided into the development (n = 441) and validation (n = 178) groups. PMI was manually measured using abdominal CT images to diagnose low muscle mass by two independent investigators. The development group's data were used to create a logistic regression model using 42 items extracted from clinical information as predictive variables; variables were selected using the stepwise method. External validity was examined using the validation group's data, and the area under the curve (AUC), sensitivity, and specificity were calculated.

resultsOf all subjects, 226 (37%) were diagnosed with low muscle mass using PMI. A predictive model for low muscle mass was calculated using ten variables: each grip strength, sex, height, dry weight, primary cause of end-stage renal disease, diastolic blood pressure at start of session, pre-dialysis potassium and albumin level, and dialysis water removal in a session. The development group's adjusted AUC, sensitivity, and specificity were 0.81, 60%, and 87%, respectively. The validation group's adjusted AUC, sensitivity, and specificity were 0.73, 64%, and 82%, respectively. DISCUSSION/

conclusionOur results facilitate skeletal muscle screening in hemodialysis patients, assisting in sarcopenia prophylaxis and intervention decisions.

Indexed as

Kidney Failure, ChronicSarcopeniaHumansMass ScreeningMuscle, SkeletalPsoas MusclesRenal DialysisRetrospective StudiesHemodialysisLow muscle mass screeningSarcopenia

Identifiers

PMID36273447
PMCPMC10210080
OpenAlexW4307042081

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.